Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network

The emerging complexity of molecular pathways implicated in mouse self-grooming behavior

Rodent self-grooming is an important complex behavior, and its deficits are translationally relevant to a wide range of neuropsychiatric disorders. Here, we analyzed a comprehensive dataset of 227 genes whose mutations are known to evoke aberrant self-grooming in mice. Using these genes, we constructed the network of their established protein-protein interactions (PPI), yielding several distinct molecular clusters related to postsynaptic density, the Wnt signaling, transcription factors, neuronal cell cycle, NOS neurotransmission, microtubule regulation, neuronal differentiation/trafficking, neurodevelopment and mitochondrial function. Utilizing further bioinformatics analyses, we also identified novel central ('hub') proteins within these clusters, whose genes may also be implicated in aberrant self-grooming and other repetitive behaviors in general. Untangling complex molecular pathways of this important behavior using in silico approaches contributes to our understanding of related neurological disorders, and may suggest novel potential targets for their pharmacological or gene therapy.

Analysis of the Protein-Protein Interaction Network Identifying c-Met as a Target of Gigantol in the Suppression of Lung Cancer Metastasis

BACKGROUND/AIM

c-Met (mesenchymal-epithelial transition factor) facilitates cancer progression and is recognized as a promising drug target. The molecular target of gigantol from Dendrobium draconis in suppressing cancer metastasis is largely unknown.

MATERIALS AND METHODS

Proteins affected by gigantol treatment were subjected to proteomic and bioinformatic analysis. Protein-Protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes (STRING). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database and hub gene were used to enrich the dominant pathways. Western blot analysis and immunofluorescence were used to validate the effect of gigantol on the target protein and signaling.

RESULTS

Gigantol down-regulates 41 adhesion proteins and 39-migratory proteins, while it up-regulates 30 adhesion-related proteins and 22 proteins controlling cell migration. The key components of our constructed PPI network comprised 41 proteins of cell adhesion enriched in 40 nodes with 25 edges, 39 proteins of cell migration enriched in 39 nodes with 76 edges in down-regulated proteins, 30 proteins of cell adhesion enriched in 30 nodes with 21 edges, and 22 proteins of cell migration enriched in 22 nodes with 22 edges in up-regulated protein. c-Met was identified as a central protein of the PPI network in the largest degree. KEGG mapper further suggested that c-Met, PI3K, and AKT were the regulatory proteins affected by gigantol. To confirm, the effects of gigantol on c-Met, the p-PI3K, PI3K, p-AKT, and AKT proteins were investigated by western blotting and the results showed a consistent effect of gigantol in the suppression of the c-Met/PI3K/AKT signal. Next, immunofluorescence showed a dramatic decrease in c-Met, PI3K and AKT activation in response to gigantol.

CONCLUSION

c-Met is an important target of gigantol treatment in lung cancer cells. Gigantol suppresses metastasis-related cell motility through decreasing c-Met resulting in PI3K/AKT signaling disruption.

Identification of monocyte-associated genes as predictive biomarkers of heart failure after acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a major contributor of heart failure (HF). Peripheral blood mononuclear cells (PBMCs), mainly monocytes, are the essential initiators of AMI-induced HF. The powerful biomarkers for early identification of AMI patients at risk of HF remain elusive. We aimed to identify monocyte-related critical genes as predictive biomarkers for post-AMI HF.

METHODS

We performed weighted gene co-expression network analysis (WGCNA) on transcriptomics of PBMCs from AMI patients who developed HF or did not. Functional enrichment analysis of genes in significant modules was performed via Metascape. Then we obtained the single-cell RNA-sequencing data of recruited monocytes/macrophages from AMI and control mice using the Scanpy and screened 381 differentially expressed genes (DEGs) between the two groups. We validated the expression changes of the 25 genes in cardiac macrophages from AMI mice based on bulk RNA-sequencing data and PBMCs data mentioned above.

RESULTS

In our study, the results of WGCNA showed that two modules containing 827 hub genes were most significantly associated with post-AMI HF, which mainly participated in cell migration, inflammation, immunity, and apoptosis. There were 25 common genes between DEGs and hub genes, showing close relationship with inflammation and collagen metabolism. CUX1, CTSD and ADD3 exhibited consistent changes in three independent studies. Receiver operating characteristic curve analysis showed that each of the three genes had excellent performance in recognizing post-AMI HF patients.

CONCLUSION

Our findings provided a set of three monocyte-related biomarkers for the early prediction of HF development after AMI as well as potential therapeutic targets of post-AMI HF.

Network-Wide Screen Identifies Variation of Novel Precise On-Module Targets Using Conformational Modudaoism

Modular targeting is promising in drug research at the network level, but it is challenging to quantificationally identify the precise on-modules. Based on a proposed Modudaoism (MD), we defined conserved MD (MDc) and varied MD (MDv) to quantitatively evaluate the conformational and energy variations of modules, and thereby identify the conserved and discrepant allosteric modules (AMs). Compared to the Z_summary , MDc/MDv got an optimized result of module preserved ratio and modular structure. In the mice anti-ischemic networks, 3, 5, and 1 conserved AMs as well as 4, 1, and 3 on-modules of baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) were identified by MDc and MDv, 5 unique AMs and their characteristic actions were revealed. Besides, co-immunoprecipitation (Co-IP) experiments validated the representative modular structure. MDc/MDv method can quantitatively define the conformational variations of modules and screen the precise on-modules network-wide, which may provide a promising strategy for drug discovery.

Comparative Proteomic Analysis of Mycobacterium tuberculosis Lineage 7 and Lineage 4 Strains Reveals Differentially Abundant Proteins Linked to Slow Growth and Virulence

In order to decipher the nature of the slowly growing Mycobacterium tuberculosis (M.tuberculosis) lineage 7, the differentially abundant proteins in strains of M. tuberculosis lineage 7 and lineage 4 were defined. Comparative proteomic analysis by mass spectrometry was employed to identify, quantitate and compare the protein profiles of strains from the two M. tuberculosis lineages. Label-free peptide quantification of whole cells from M. tuberculosis lineage 7 and 4 yielded the identification of 2825 and 2541 proteins, respectively. A combined total of 2867 protein groups covering 71% of the predicted M. tuberculosis proteome were identified. The abundance of 125 proteins in M. tuberculosis lineage 7 and 4 strains was significantly altered. Notably, the analysis showed that a number of M. tuberculosis proteins involved in growth and virulence were less abundant in lineage 7 strains compared to lineage 4. Five ABC transporter proteins, three phosphate binding proteins essential for inorganic phosphate uptake, and six components of the type 7 secretion system ESX-3 involved in iron acquisition were less abundant in M. tuberculosis lineage 7. This proteogenomic analysis provided an insight into the lineage 7-specific protein profile which may provide clues to understanding the differential properties of lineage 7 strains in terms of slow growth, survival fitness, and pathogenesis.

Comparative proteomic analysis of Neisseria meningitidis wildtype and dprA null mutant strains links DNA processing to pilus biogenesis

Background

DNA processing chain A (DprA) is a DNA binding protein which is ubiquitous in bacteria, and is required for DNA transformation to various extents among bacterial species. However, the interaction of DprA with competence and recombination proteins is poorly understood. Therefore, the proteomes of whole Neisseria meningitidis (Nm) wildtype and dprA mutant cells were compared. Such a comparative proteomic analysis increases our understanding of the interactions of DprA with other Nm components and may elucidate its potential role beyond DNA processing in transformation.

Results

Using label-free quantitative proteomics, a total of 1057 unique Nm proteins were identified, out of which 100 were quantified as differentially abundant (P ≤ 0.05 and fold change ≥ |2|) in the dprA null mutant. Proteins involved in homologous recombination (RecA, UvrD and HolA), pilus biogenesis (PilG, PilT1, PilT2, PilM, PilO, PilQ, PilF and PilE), cell division, including core energy metabolism, and response to oxidative stress were downregulated in the Nm dprA null mutant. The mass spectrometry data are available via ProteomeXchange with identifier PXD006121. Immunoblotting and co-immunoprecipitation were employed to validate the association of DprA with PilG. The analysis revealed reduced amounts of PilG in the dprA null mutant and reduced amounts of DprA in the Nm pilG null mutant. Moreover, a number of pilus biogenesis proteins were shown to interact with DprA and /or PilG.

Conclusions

DprA interacts with proteins essential for Nm DNA recombination in transformation, pilus biogenesis, and other functions associated with the inner membrane. Inverse downregulation of Nm DprA and PilG expression in the corresponding mutants indicates a link between DNA processing and pilus biogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-1004-8) contains supplementary material, which is available to authorized users.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Mining kidney toxicogenomic data by using gene co-expression modules

Background

Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.

Results

We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.

Conclusions

Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.

Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein-protein or gene-gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.

Systems level analysis and identification of pathways and networks associated with liver fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Gene expression in rat models for inter-generational transmission of islet dysfunction and obesity

Paternal high fat diet (HFD) consumption triggers unique gene signatures, consistent with premature aging and chronic degenerative disorders, in both white adipose tissue (RpWAT) and pancreatic islets of daughters. In addition to published data in Nature, 2010, 467, 963-966 (GSE: 19877, islet) and FASEB J 2014, 28, 1830-1841 (GSE: 33551, RpWAT), we describe here additional details on systems-based approaches and analysis to develop our observations. Our data provides a resource for exploring the complex molecular mechanisms that underlie intergenerational transmission of obesity.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.

Daucosterol promotes the proliferation of neural stem cells

Neural stem cells (NSCs) are self-regenerating cells, but their regenerative capacity is limited. The present study was conducted to investigate the effect of daucosterol (a sterolin) on the promotion of NSC proliferation and determine the corresponding molecular mechanism. Results of cell counting kit-8 (CCK-8) assay showed that daucosterol significantly increased the quantity of viable cells and the effectiveness of daucosterol was similar to that of basic fibroblast growth factor (bFGF) and epidermal growth factor (EGF). Flow cytometry detection of CFSE-labeled (CFSE, carboxyfluorescein diacetate succinimidyl ester) NSCs showed that Div Index (or the average number of cell divisions) and % Divided (or the percentage of cells that divided at least once) of the cells were increased, indicating that daucosterol increased the percentage of NSCs re-entering the cell cycle. mRNA microarray analysis showed that 333 genes that are mostly involved in the mitotic cell cycle were up-regulated. By contrast, 627 genes that are mostly involved in differentiation were down-regulated. In particular, insulin-like growth factor I (IGF1) was considered as an important regulatory gene that functionally promoted NSC proliferation, and the increased expression of IGF1 protein was validated by ELISA. In addition, the phosphorylation of AKT was increased, indicating that the proliferation-enhancing activity of daucosterol may be involved in IGF1-AKT pathway. Our study provided information about daucosterol as an efficient and inexpensive growth factor alternative that could be used in clinical medicine and research applications.

Microarray Analysis of mRNA and MicroRNA Expression Profile Reveals the Role of β -Sitosterol-D-glucoside in the Proliferation of Neural Stem Cell

Neural stem cells (NSCs) are self-regenerating cells, but their regenerative capacity is limited. The present study was conducted to investigate the effect of β-sitosterol-D-glucoside (BSSG) on the proliferation of hippocampal NSCs and to determine the corresponding molecular mechanism. Results of CCK-8 assay showed that BSSG significantly increased NSC proliferation and the effectiveness of BSSG was similar to that of basic fibroblast growth factor and epidermal growth factor. mRNA expression profiling showed that 960 genes were differentially expressed after NSCs were treated with BSSG. Among the 960 genes, IGF1 is considered as a key regulatory gene that functionally promotes NSC proliferation. MicroRNA (miRNA) expression profiling indicated that 30 and 84 miRNAs were upregulated and downregulated, respectively. miRNA-mRNA relevance analysis revealed that numerous mRNAs including IGF1 mRNA were negatively regulated by miRNAs with decreased expression, thereby increasing the corresponding mRNA expression. The increased expression of IGF1 protein was validated by ELISA. Picropodophyllin (PPP, an inhibitor of IGF-1R) inhibition test confirmed that the proliferation-enhancing effect depended on IGF1. This study provided information about BSSG as an efficient and inexpensive growth factor alternative, of which the effect is closely involved in IGF1.

Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease

Background

This study aims to expand knowledge of the complex process of myocardial infarction (MI) through the application of a systems-based approach.

Methods

We generated a gene co-expression network from microarray data originating from a mouse model of MI. We characterized it on the basis of connectivity patterns and independent biological information. The potential clinical novelty and relevance of top predictions were assessed in the context of disease classification models. Models were validated using independent gene expression data from mouse and human samples.

Results

The gene co-expression network consisted of 178 genes and 7298 associations. The network was dissected into statistically and biologically meaningful communities of highly interconnected and co-expressed genes. Among the most significant communities, one was distinctly associated with molecular events underlying heart repair after MI (P < 0.05). Col5a2, a gene previously not specifically linked to MI response but responsible for the classic type of Ehlers-Danlos syndrome, was found to have many and strong co-expression associations within this community (11 connections with ρ > 0.85). To validate the potential clinical application of this discovery, we tested its disease discriminatory capacity on independently generated MI datasets from mice and humans. High classification accuracy and concordance was achieved across these evaluations with areas under the receiving operating characteristic curve above 0.8.

Conclusion

Network-based approaches can enable the discovery of clinically-interesting predictive insights that are accurate and robust. Col5a2 shows predictive potential in MI, and in principle may represent a novel candidate marker for the identification and treatment of ischemic cardiovascular disease.

A gender-related action of IFNbeta-therapy was found in multiple sclerosis

Background

Understanding how sexual dimorphism affects the physiological and pathological responses of the immune system is of considerable clinical importance and could lead to new approaches in therapy. Sexual dimorphism has already been noted as an important factor in autoimmune diseases: the aim of this study was to establish whether sexual dimorphism in autoimmune diseases is the result of differing pathways being involved in the regulation of T-helper (Th) cell network homeostasis.

Methods

We focused on sexually dimorphic changes in the immune response in multiple sclerosis (MS) patients in order to ascertain how these alterations relate to the pathway regulation of the cytokine homeostasis and the Th cell networks. We studied antigen presenting cell (APC)-dependent T cell activation in groups of healthy subjects, in patients under interferon (IFN) β-therapy and untreated. Cytokines, soluble (s) CD30 and the expanded disability status scale (EDSS) were used as biomarkers for T cell differentiation and neurological deficit.

Results

The data confirm our belief that sexual dimorphism in autoimmune diseases is the result of differing pathways that regulate Th cell network homeostasis: interleukin (IL) 6 pathways in women and IFNγ pathways in men. Given the increased susceptibility of women to MS and the significance of IL6 in the autoimmune process compared to IFNγ, it is logical to assume that IL6 pathways are in some way implicated in the prevalence of autoimmune diseases in women. Indeed, our data indicate that IL6 pathways are also involved in T regulatory (Treg) cell imbalance and an increase in neurological deficit in both men and women groups of MS patients, underlining the autoimmune etiology of multiple sclerosis. In further support of differing cytokine pathways in men and women, we noted that the efficacy of IFNβ-treatment in the re-establishment of Th-network balance and in the delaying of the neurological disability progression is linked to the IL6 pathway in women, but to the IFNγ pathway in men. Lastly, we also identified specific gender biomarkers for the use in therapy.

Conclusions

The identification of gender-specific drugs is of considerable importance in translational medicine and will undoubtedly lead to more appropriate therapeutic strategies and more successful treatment.

Lung cancer signature biomarkers: tissue specific semantic similarity based clustering of digital differential display (DDD) data

BACKGROUND

The tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used 'Gene Ontology semantic similarity score' to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability.

RESULTS

Subsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1-4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4).

CONCLUSIONS

Expression analysis reveals that hypoxia induced lung cancer related biomarkers (panel 3), HIF and its modulating proteins (TGM2, CSNK1A1, CTNNA1, NAMPT/Visfatin, TNFRSF1A, ETS1, SRC-1, FN1, APLP2, DMBT1/SAG, AIB1 and AZIN1) are significantly down regulated. All down regulated genes in this panel were highly up regulated in most other types of cancers. These panels of proteins may represent signature biomarkers for lung cancer and will aid in lung cancer diagnosis and disease monitoring as well as in the prediction of responses to therapeutics.

Gender-specific cytokine pathways, targets, and biomarkers for the switch from health to adenoma and colorectal cancer

Studies focusing on gender have shown that differences exist in how the immune system responds to disease and therapy. Understanding how gender influences immunological mechanisms in health and disease and identifying gender-specific biomarkers could lead to specifically tailored treatment and ultimately improve therapeutic success rates. T helper1 (Th1) and Th2 cytokines (Th1/Th2) have pivotal roles in the homeostasis of Th1 and Th2 cell network functions in the immune response but sex steroids affect Th1/Th2 production in different ways and a natural sexual dimorphism in the immune response has been shown. In order to investigate these differences further, we developed Th-cytokine data-driven models of the immune response and evaluated healthy subject peripheral blood samples. Independent cohorts of colorectal cancer and adenoma patients were also studied for comparison purposes. Our results show that the interferon (IFN)γ production pathway for immune response homeostasis is specific to men whilst the interleukin- (IL-) 6 production pathway for immune response homeostasis is specific to women. The IL-10 pathway for restoring immune system resting homeostasis was common to both but was controlled by the respective gender-specific pathways. These gender pathways could well be used as targets and biomarkers in translational research into developing new clinical strategies.

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

Background

Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context.

Findings

We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology.

Conclusion

FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga.

Multimarker panels in sepsis

Biomarkers differentiate between 2 or more biologic states. The complexity of diseases like sepsis makes it unlikely that any single marker will allow for precise disease specification. Combining several biomarkers into a single classification rule should help to improve their accuracy and, therefore, their usefulness. This article reviews several studies using multimarker panels, and highlights the potential of more sophisticated diagnostic and prognostic techniques in future multimarker panels. More complex algorithms should accelerate the adoption of multimarker panels into the routine management of patients with sepsis, provided that clinicians understand the multimarker approach.

Drug-target network in myocardial infarction reveals multiple side effects of unrelated drugs

The systems-level characterization of drug-target associations in myocardial infarction (MI) has not been reported to date. We report a computational approach that combines different sources of drug and protein interaction information to assemble the myocardial infarction drug-target interactome network (My-DTome). My-DTome comprises approved and other drugs interlinked in a single, highly-connected network with modular organization. We show that approved and other drugs may both be highly connected and represent network bottlenecks. This highlights influential roles for such drugs on seemingly unrelated targets and pathways via direct and indirect interactions. My-DTome modules are associated with relevant molecular processes and pathways. We find evidence that these modules may be regulated by microRNAs with potential therapeutic roles in MI. Different drugs can jointly impact a module. We provide systemic insights into cardiovascular effects of non-cardiovascular drugs. My-DTome provides the basis for an alternative approach to investigate new targets and multidrug treatment in MI.

Information encoded in a network of inflammation proteins predicts clinical outcome after myocardial infarction

Background

Inflammation plays an important role in cardiac repair after myocardial infarction (MI). Nevertheless, the systems-level characterization of inflammation proteins in MI remains incomplete. There is a need to demonstrate the potential value of molecular network-based approaches to translational research. We investigated the interplay of inflammation proteins and assessed network-derived knowledge to support clinical decisions after MI. The main focus is the prediction of clinical outcome after MI.

Methods

We assembled My-Inflamome, a network of protein interactions related to inflammation and prognosis in MI. We established associations between network properties, disease biology and capacity to distinguish between prognostic categories. The latter was tested with classification models built on blood-derived microarray data from post-MI patients with different outcomes. This was followed by experimental verification of significant associations.

Results

My-Inflamome is organized into modules highly specialized in different biological processes relevant to heart repair. Highly connected proteins also tend to be high-traffic components. Such bottlenecks together with genes extracted from the modules provided the basis for novel prognostic models, which could not have been uncovered by standard analyses. Modules with significant involvement in transcriptional regulation are targeted by a small set of microRNAs. We suggest a new panel of gene expression biomarkers (TRAF2, SHKBP1 and UBC) with high discriminatory capability. Follow-up validations reported promising outcomes and motivate future research.

Conclusion

This study enhances understanding of the interaction network that executes inflammatory responses in human MI. Network-encoded information can be translated into knowledge with potential prognostic application. Independent evaluations are required to further estimate the clinical relevance of the new prognostic genes.

Proteomic analysis of plasma samples from patients with acute myocardial infarction identifies haptoglobin as a potential prognostic biomarker

Prognosis of clinical outcome following myocardial infarction is variable and difficult to predict. We have analyzed the plasma proteome of thirty patients with acute myocardial infarction to search for new prognostic biomarkers. Proteomic analyses of blood samples were performed by 2-D-DiGE after plasma depletion of albumin and immunoglobulins G. New York Heart Association (NYHA) class determined at 1-year follow-up was used to identify patients with heart failure. Principal component analysis and hierarchical clustering of proteomic data revealed that patients could be separated into 3 groups. The 22 differentially expressed proteins involved in this grouping were identified as haptoglobin (Hp) and respective isoforms. The 3 groups of patients had distinct Hp isoforms: patients from group 1 had the α1-α1, patients from group 2 the α2-α1, and patients from group 3 the α2-α2 genotype. This classification was also associated with different total plasma levels of Hp. The presence of the α2 genotype and low plasma levels of Hp was associated with a higher NYHA class and therefore with a detrimental functional outcome after myocardial infarction. A plasma level of Hp below 1.4g/L predicted the occurrence of heart failure (NYHA 2, 3, 4) at 1-year with 100% sensitivity.

Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cells

Background

Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs.

Results

The predictive integration of gene expression data and standardized functional similarity information enabled us to identify new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement and evaluate an integrated prediction approach based on the concept of k-nearest neighbours learning (kNN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures. The resulting integrated kNN system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating characteristic curve > 0.8). We also showed that the proposed models can outperform those discovered by standard gene expression analysis. Furthermore, we report an initial independent in vitro experimental follow-up, which provides additional evidence of the potential validity of the top biosignature.

Conclusion

Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker discovery problems.

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms.

Prognostic transcriptional association networks: a new supervised approach based on regression trees

Motivation: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains.

Results: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine.

Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/.

Contact:inepomuceno@us.es

Supplementary information:Supplementary data are available at Bioinformatics online.